The Consortium for Computational Physics and Chemistry (CCPC) is a joint core research and development activity among five national laboratories that has been created to utilize unique DOE computational modeling facilities and experience in order to accelerate the discovery and deployment of novel materials in support of the Energy Materials Network and the Materials Genome Initiative. The CCPC also computationally bridges the chemical reactions occurring at the nanoscale to reactor- and plant-scale processes to enable critical experimental verifications in the BETO program and predict the effects of process scale-up on the techno-economic analysis of commercial scale operations.
- Process and reactor models that incorporate feedstock and catalyst properties, to guide reactor design and operation and inform and understand reactor scale up from lab, to pilot, to commercial scales.
- Catalyst models that provide active site energetics and entropics to guide the experimental design and discovery of next generation catalysts and improve durability and cost-effectiveness.
Questions about the CCPC should be directed towards Jim Parks.
Mission and Objectives
Mission: To utilize core computational capabilities across the US DOE national laboratory system to enable and accelerate the development of new materials and optimize process scale-up to advance the bioenergy economy.
Vision: The computational toolset developed by CCPC facilitates the modeling of biomass industrial technologies from atomic to process scales, thereby reducing the cost, time, and risk in commercializing bioenergy technologies.
- Provide predictive simulation tools to enable BETO experimental teams to maximize yield and fuel chemistry based on reactor design, operational parameters, feedstock type, and feedstock particle size distributions during pilot scale verification.
- Simulations of reactor scale up effects and predictive impact on linkage of BETO bench and pilot scale results to full plant TEA.
- In conjunction with ChemCatBio, more rapid advancements in catalyst formulation and design that result in experimentally observed improvements (yield, selectivity, durability, lifetime, cost).
We are focusing on technical barriers and questions associated with 3 major areas of bio-oil production via fast pyrolysis:
- Links between feedstock properties and preparation and pyrolysis oil quality
- The potential benefits of in-situ and ex-situ catalytic vapor phase upgrading
- Ways to improve catalyst performance in liquid phase upgrading
The CCPC team uses computational models of the rate and quality limiting reaction and transport kinetics in key process stages to identify and resolve major bottlenecks and uncertainties:
- Understanding and minimizing fast pyrolysis carbon loss
- Improving bio-oil properties (e.g. reduce oxygen, corrosivity, viscosity, and instability) with vapor and liquid-phase catalysis
- Improving aqueous phase bio-oil component utilization
- Improving understanding of process scale-up and integration
- Improving predictions needed for Techno-Economic Analysis (TEA) and Life Cycle Analysis (LCA)
The modeling tools we employ make use of unique national lab experimental data and computational facilities that cover a broad range of scales from atomistic and moelcular chemistry to single particle and full reactor simulations. To maximize the relevance of our results to industry and evaluations of strategic options for bio-oil production, we are also creating low-order implementations of our models that can be used for rapid screening studies without requiring special computational facilities.
Collaboration Between National Labs Spans Full Conversion Process
Labs in the CCPC work together to cover all aspects of the biomass to bioproduct conversion process:
Additional information regarding points of contact and R&D activities at each CCPC lab can be accessed by clicking on the appropriate links at the top of this website.
Our Unique Management Approach
The CCPC utilizes input from a unique combination of industry advisors, core experimental projects, and academic partners to guide selection and priority of projects.